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KMID : 0371020090420020135
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Journal of Preventive Medicine and Public Health
2009 Volume.42 No. 2 p.135 ~ p.142
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An Evaluation of Sampling Design for Estimating an Epidemiologic Volume of Diabetes and for Assessing Present Status of Its Control in Korea
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Lee Ji-Sung
Baik Sei-Hyun
Park Ie-Byung
Lee June-Young
Kim Jai-Yong
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Abstract
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Objectives: An appropriate sampling strategy for estimating an epidemiologic volume of diabetes has been evaluated through a simulation.
Methods: We analyzed about 250 million medical insurance claims data submitted to the Health Insurance Review & Assessment Service with diabetes as principal or subsequent diagnoses, more than or equal to once per year, in 2003. The database was re-constructed to a ’patient-hospital profile’ that had 3,676,164 cases, and then to a ’patient profile’ that consisted of 2,412,082 observations. The patient profile data was then used to test the validity of a proposed sampling frame and methods of sampling to develop diabetic-related epidemiologic indices.
Results: Simulation study showed that a use of a stratified two-stage cluster sampling design with a total sample size of 4,000 will provide an estimate of 57.04% (95% prediction range, 49.83 - 64.24%) for a treatment prescription rate of diabetes. The proposed sampling design consists, at first, stratifying the area of the nation into "metropolitan/city/county" and the types of hospital into "tertiary/secondary/primary/clinic" with a proportion of 5:10:10:75. Hospitals were then randomly selected within the strata as a primary sampling unit, followed by a random selection of patients within the hospitals as a secondly sampling unit. The difference between the estimate and the parameter value was projected to be less than 0.3%.
Conclusions: The sampling scheme proposed will be applied to a subsequent nationwide field survey not only for estimating the epidemiologic volume of diabetes but also for assessing the present status of nationwide diabetes control.
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KEYWORD
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Simulation, Diabetes mellitus, Epidemiologic indices, Insurance claim review data, Stratified two-stage cluster sampling
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